{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LEARNING\n",
    "\n",
    "This notebook serves as supporting material for topics covered in **Chapter 18 - Learning from Examples** , **Chapter 19 - Knowledge in Learning**, **Chapter 20 - Learning Probabilistic Models** from the book *Artificial Intelligence: A Modern Approach*. This notebook uses implementations from [learning.py](https://github.com/aimacode/aima-python/blob/master/learning.py). Let's start by importing everything from the module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from learning import *\n",
    "from notebook import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CONTENTS\n",
    "\n",
    "* Machine Learning Overview\n",
    "* Datasets\n",
    "* Iris Visualization\n",
    "* Distance Functions\n",
    "* Plurality Learner\n",
    "* k-Nearest Neighbours\n",
    "* Decision Tree Learner\n",
    "* Random Forest Learner\n",
    "* Naive Bayes Learner\n",
    "* Perceptron\n",
    "* Learner Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## MACHINE LEARNING OVERVIEW\n",
    "\n",
    "In this notebook, we learn about agents that can improve their behavior through diligent study of their own experiences.\n",
    "\n",
    "An agent is **learning** if it improves its performance on future tasks after making observations about the world.\n",
    "\n",
    "There are three types of feedback that determine the three main types of learning:\n",
    "\n",
    "* **Supervised Learning**:\n",
    "\n",
    "In Supervised Learning the agent observes some example input-output pairs and learns a function that maps from input to output.\n",
    "\n",
    "**Example**: Let's think of an agent to classify images containing cats or dogs. If we provide an image containing a cat or a dog, this agent should output a string \"cat\" or \"dog\" for that particular image. To teach this agent, we will give a lot of input-output pairs like {cat image-\"cat\"}, {dog image-\"dog\"} to the agent. The agent then learns a function that maps from an input image to one of those strings.\n",
    "\n",
    "* **Unsupervised Learning**:\n",
    "\n",
    "In Unsupervised Learning the agent learns patterns in the input even though no explicit feedback is supplied. The most common type is **clustering**: detecting potential useful clusters of input examples.\n",
    "\n",
    "**Example**: A taxi agent would develop a concept of *good traffic days* and *bad traffic days* without ever being given labeled examples.\n",
    "\n",
    "* **Reinforcement Learning**:\n",
    "\n",
    "In Reinforcement Learning the agent learns from a series of reinforcements—rewards or punishments.\n",
    "\n",
    "**Example**: Let's talk about an agent to play the popular Atari game—[Pong](http://www.ponggame.org). We will reward a point for every correct move and deduct a point for every wrong move from the agent. Eventually, the agent will figure out its actions prior to reinforcement were most responsible for it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DATASETS\n",
    "\n",
    "For the following tutorials we will use a range of datasets, to better showcase the strengths and weaknesses of the algorithms. The datasests are the following:\n",
    "\n",
    "* [Fisher's Iris](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/iris.csv): Each item represents a flower, with four measurements: the length and the width of the sepals and petals. Each item/flower is categorized into one of three species: Setosa, Versicolor and Virginica.\n",
    "\n",
    "* [Zoo](https://github.com/aimacode/aima-data/blob/a21fc108f52ad551344e947b0eb97df82f8d2b2b/zoo.csv): The dataset holds different animals and their classification as \"mammal\", \"fish\", etc. The new animal we want to classify has the following measurements: 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1 (don't concern yourself with what the measurements mean)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make using the datasets easier, we have written a class, `DataSet`, in `learning.py`. The tutorials found here make use of this class.\n",
    "\n",
    "Let's have a look at how it works before we get started with the algorithms."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intro\n",
    "\n",
    "A lot of the datasets we will work with are .csv files (although other formats are supported too). We have a collection of sample datasets ready to use [on aima-data](https://github.com/aimacode/aima-data/tree/a21fc108f52ad551344e947b0eb97df82f8d2b2b). Two examples are the datasets mentioned above (*iris.csv* and *zoo.csv*). You can find plenty datasets online, and a good repository of such datasets is [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets.html).\n",
    "\n",
    "In such files, each line corresponds to one item/measurement. Each individual value in a line represents a *feature* and usually there is a value denoting the *class* of the item.\n",
    "\n",
    "You can find the code for the dataset here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%psource DataSet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class Attributes\n",
    "\n",
    "* **examples**: Holds the items of the dataset. Each item is a list of values.\n",
    "\n",
    "* **attrs**: The indexes of the features (by default in the range of [0,f), where *f* is the number of features). For example, `item[i]` returns the feature at index *i* of *item*.\n",
    "\n",
    "* **attrnames**: An optional list with attribute names. For example, `item[s]`, where *s* is a feature name, returns the feature of name *s* in *item*.\n",
    "\n",
    "* **target**: The attribute a learning algorithm will try to predict. By default the last attribute.\n",
    "\n",
    "* **inputs**: This is the list of attributes without the target.\n",
    "\n",
    "* **values**: A list of lists which holds the set of possible values for the corresponding attribute/feature. If initially `None`, it gets computed (by the function `setproblem`) from the examples.\n",
    "\n",
    "* **distance**: The distance function used in the learner to calculate the distance between two items. By default `mean_boolean_error`.\n",
    "\n",
    "* **name**: Name of the dataset.\n",
    "\n",
    "* **source**: The source of the dataset (url or other). Not used in the code.\n",
    "\n",
    "* **exclude**: A list of indexes to exclude from `inputs`. The list can include either attribute indexes (attrs) or names (attrnames)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class Helper Functions\n",
    "\n",
    "These functions help modify a `DataSet` object to your needs.\n",
    "\n",
    "* **sanitize**: Takes as input an example and returns it with non-input (target) attributes replaced by `None`. Useful for testing. Keep in mind that the example given is not itself sanitized, but instead a sanitized copy is returned.\n",
    "\n",
    "* **classes_to_numbers**: Maps the class names of a dataset to numbers. If the class names are not given, they are computed from the dataset values. Useful for classifiers that return a numerical value instead of a string.\n",
    "\n",
    "* **remove_examples**: Removes examples containing a given value. Useful for removing examples with missing values, or for removing classes (needed for binary classifiers)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing a Dataset\n",
    "\n",
    "#### Importing from aima-data\n",
    "\n",
    "Datasets uploaded on aima-data can be imported with the following line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = DataSet(name=\"iris\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check that we imported the correct dataset, we can do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.1, 3.5, 1.4, 0.2, 'setosa']\n",
      "[0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "print(iris.examples[0])\n",
    "print(iris.inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which correctly prints the first line in the csv file and the list of attribute indexes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When importing a dataset, we can specify to exclude an attribute (for example, at index 1) by setting the parameter `exclude` to the attribute index or name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "iris2 = DataSet(name=\"iris\",exclude=[1])\n",
    "print(iris2.inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attributes\n",
    "\n",
    "Here we showcase the attributes.\n",
    "\n",
    "First we will print the first three items/examples in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[5.1, 3.5, 1.4, 0.2, 'setosa'], [4.9, 3.0, 1.4, 0.2, 'setosa'], [4.7, 3.2, 1.3, 0.2, 'setosa']]\n"
     ]
    }
   ],
   "source": [
    "print(iris.examples[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we will print `attrs`, `attrnames`, `target`, `input`. Notice how `attrs` holds values in [0,4], but since the fourth attribute is the target, `inputs` holds values in [0,3]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "attrs: [0, 1, 2, 3, 4]\n",
      "attrnames (by default same as attrs): [0, 1, 2, 3, 4]\n",
      "target: 4\n",
      "inputs: [0, 1, 2, 3]\n"
     ]
    }
   ],
   "source": [
    "print(\"attrs:\", iris.attrs)\n",
    "print(\"attrnames (by default same as attrs):\", iris.attrnames)\n",
    "print(\"target:\", iris.target)\n",
    "print(\"inputs:\", iris.inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will print all the possible values for the first feature/attribute."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4.7, 5.5, 6.3, 5.0, 4.9, 5.1, 4.6, 5.4, 4.4, 4.8, 5.8, 7.0, 7.1, 4.5, 5.9, 5.6, 6.9, 6.6, 6.5, 6.4, 6.0, 6.1, 7.6, 7.4, 7.9, 4.3, 5.7, 5.3, 5.2, 6.7, 6.2, 6.8, 7.3, 7.2, 7.7]\n"
     ]
    }
   ],
   "source": [
    "print(iris.values[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we will print the dataset's name and source. Keep in mind that we have not set a source for the dataset, so in this case it is empty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name: iris\n",
      "source: \n"
     ]
    }
   ],
   "source": [
    "print(\"name:\", iris.name)\n",
    "print(\"source:\", iris.source)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A useful combination of the above is `dataset.values[dataset.target]` which returns the possible values of the target. For classification problems, this will return all the possible classes. Let's try it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa', 'virginica', 'versicolor']\n"
     ]
    }
   ],
   "source": [
    "print(iris.values[iris.target])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will now take a look at the auxiliary functions found in the class.\n",
    "\n",
    "First we will take a look at the `sanitize` function, which sets the non-input values of the given example to `None`.\n",
    "\n",
    "In this case we want to hide the class of the first example, so we will sanitize it.\n",
    "\n",
    "Note that the function doesn't actually change the given example; it returns a sanitized *copy* of it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sanitized: [5.1, 3.5, 1.4, 0.2, None]\n",
      "Original: [5.1, 3.5, 1.4, 0.2, 'setosa']\n"
     ]
    }
   ],
   "source": [
    "print(\"Sanitized:\",iris.sanitize(iris.examples[0]))\n",
    "print(\"Original:\",iris.examples[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Currently the `iris` dataset has three classes, setosa, virginica and versicolor. We want though to convert it to a binary class dataset (a dataset with two classes). The class we want to remove is \"virginica\". To accomplish that we will utilize the helper function `remove_examples`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['setosa', 'versicolor']\n"
     ]
    }
   ],
   "source": [
    "iris2 = DataSet(name=\"iris\")\n",
    "\n",
    "iris2.remove_examples(\"virginica\")\n",
    "print(iris2.values[iris2.target])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also have `classes_to_numbers`. For a lot of the classifiers in the module (like the Neural Network), classes should have numerical values. With this function we map string class names to numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class of first example: setosa\n",
      "Class of first example: 0\n"
     ]
    }
   ],
   "source": [
    "print(\"Class of first example:\",iris2.examples[0][iris2.target])\n",
    "iris2.classes_to_numbers()\n",
    "print(\"Class of first example:\",iris2.examples[0][iris2.target])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see \"setosa\" was mapped to 0."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we take a look at `find_means_and_deviations`. It finds the means and standard deviations of the features for each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setosa feature means: [5.006, 3.418, 1.464, 0.244]\n",
      "Versicolor mean for first feature: 5.936\n",
      "Setosa feature deviations: [0.3524896872134513, 0.38102439795469095, 0.17351115943644546, 0.10720950308167838]\n",
      "Virginica deviation for second feature: 0.32249663817263746\n"
     ]
    }
   ],
   "source": [
    "means, deviations = iris.find_means_and_deviations()\n",
    "\n",
    "print(\"Setosa feature means:\", means[\"setosa\"])\n",
    "print(\"Versicolor mean for first feature:\", means[\"versicolor\"][0])\n",
    "\n",
    "print(\"Setosa feature deviations:\", deviations[\"setosa\"])\n",
    "print(\"Virginica deviation for second feature:\",deviations[\"virginica\"][1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## IRIS VISUALIZATION\n",
    "\n",
    "Since we will use the iris dataset extensively in this notebook, below we provide a visualization tool that helps in comprehending the dataset and thus how the algorithms work.\n",
    "\n",
    "We plot the dataset in a 3D space using `matplotlib` and the function `show_iris` from `notebook.py`. The function takes as input three parameters, *i*, *j* and *k*, which are indicises to the iris features, \"Sepal Length\", \"Sepal Width\", \"Petal Length\" and \"Petal Width\" (0 to 3). By default we show the first three features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ww/nz5wEABw4cQHNzs+wnafGVvZwnxuXlZQwPD4NlWdx6660IBAJlP34hARGN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\npSqjJ1eBBEFQLFrT09OIRqOgaRo1NTWIRqMIBoNpp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JKg1uAnQDRmcblcsFgsUjUhF6SK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ZQnr27EmysrLe6/g5c+aQzz77rEXG8vr1a+Lk5ERMTU1J3759iZmZGfnll1+ISCQit27dIvb2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\nnjD6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72c8967390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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X4fV6FXOjVKt1cjtGFkqdDSFHZOG73/0uAOCBBx7IuP3555/H448/DgCYnp7O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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72c8967400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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XbwQ8EQsFTCYVDplA0zT8fj/OnDkDn8+H1tZW2dozF2rOglj62dPTA41Ggy1b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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f72c86b0cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris = DataSet(name=\"iris\")\n",
    "\n",
    "show_iris()\n",
    "show_iris(0, 1, 3)\n",
    "show_iris(1, 2, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can play around with the values to get a good look at the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DISTANCE FUNCTIONS\n",
    "\n",
    "In a lot of algorithms (like the *k-Nearest Neighbors* algorithm), there is a need to compare items, finding how *similar* or *close* they are. For that we have many different functions at our disposal. Below are the functions implemented in the module:\n",
    "\n",
    "### Manhattan Distance (`manhattan_distance`)\n",
    "\n",
    "One of the simplest distance functions. It calculates the difference between the coordinates/features of two items. To understand how it works, imagine a 2D grid with coordinates *x* and *y*. In that grid we have two items, at the squares positioned at `(1,2)` and `(3,4)`. The difference between their two coordinates is `3-1=2` and `4-2=2`. If we sum these up we get `4`. That means to get from `(1,2)` to `(3,4)` we need four moves; two to the right and two more up. The function works similarly for n-dimensional grids."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Manhattan Distance between (1,2) and (3,4) is 4\n"
     ]
    }
   ],
   "source": [
    "def manhattan_distance(X, Y):\n",
    "    return sum([abs(x - y) for x, y in zip(X, Y)])\n",
    "\n",
    "\n",
    "distance = manhattan_distance([1,2], [3,4])\n",
    "print(\"Manhattan Distance between (1,2) and (3,4) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Euclidean Distance (`euclidean_distance`)\n",
    "\n",
    "Probably the most popular distance function. It returns the square root of the sum of the squared differences between individual elements of two items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Euclidean Distance between (1,2) and (3,4) is 2.8284271247461903\n"
     ]
    }
   ],
   "source": [
    "def euclidean_distance(X, Y):\n",
    "    return math.sqrt(sum([(x - y)**2 for x, y in zip(X,Y)]))\n",
    "\n",
    "\n",
    "distance = euclidean_distance([1,2], [3,4])\n",
    "print(\"Euclidean Distance between (1,2) and (3,4) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hamming Distance (`hamming_distance`)\n",
    "\n",
    "This function counts the number of differences between single elements in two items. For example, if we have two binary strings \"111\" and \"011\" the function will return 1, since the two strings only differ at the first element. The function works the same way for non-binary strings too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hamming Distance between 'abc' and 'abb' is 1\n"
     ]
    }
   ],
   "source": [
    "def hamming_distance(X, Y):\n",
    "    return sum(x != y for x, y in zip(X, Y))\n",
    "\n",
    "\n",
    "distance = hamming_distance(['a','b','c'], ['a','b','b'])\n",
    "print(\"Hamming Distance between 'abc' and 'abb' is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Boolean Error (`mean_boolean_error`)\n",
    "\n",
    "To calculate this distance, we find the ratio of different elements over all elements of two items. For example, if the two items are `(1,2,3)` and `(1,4,5)`, the ration of different/all elements is 2/3, since they differ in two out of three elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Boolean Error Distance between (1,2,3) and (1,4,5) is 0.6666666666666666\n"
     ]
    }
   ],
   "source": [
    "def mean_boolean_error(X, Y):\n",
    "    return mean(int(x != y) for x, y in zip(X, Y))\n",
    "\n",
    "\n",
    "distance = mean_boolean_error([1,2,3], [1,4,5])\n",
    "print(\"Mean Boolean Error Distance between (1,2,3) and (1,4,5) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Error (`mean_error`)\n",
    "\n",
    "This function finds the mean difference of single elements between two items. For example, if the two items are `(1,0,5)` and `(3,10,5)`, their error distance is `(3-1) + (10-0) + (5-5) = 2 + 10 + 0 = 12`. The mean error distance therefore is `12/3=4`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Error Distance between (1,0,5) and (3,10,5) is 4\n"
     ]
    }
   ],
   "source": [
    "def mean_error(X, Y):\n",
    "    return mean([abs(x - y) for x, y in zip(X, Y)])\n",
    "\n",
    "\n",
    "distance = mean_error([1,0,5], [3,10,5])\n",
    "print(\"Mean Error Distance between (1,0,5) and (3,10,5) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean Square Error (`ms_error`)\n",
    "\n",
    "This is very similar to the `Mean Error`, but instead of calculating the difference between elements, we are calculating the *square* of the differences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Square Distance between (1,0,5) and (3,10,5) is 34.666666666666664\n"
     ]
    }
   ],
   "source": [
    "def ms_error(X, Y):\n",
    "    return mean([(x - y)**2 for x, y in zip(X, Y)])\n",
    "\n",
    "\n",
    "distance = ms_error([1,0,5], [3,10,5])\n",
    "print(\"Mean Square Distance between (1,0,5) and (3,10,5) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Root of Mean Square Error (`rms_error`)\n",
    "\n",
    "This is the square root of `Mean Square Error`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Root of Mean Error Distance between (1,0,5) and (3,10,5) is 5.887840577551898\n"
     ]
    }
   ],
   "source": [
    "def rms_error(X, Y):\n",
    "    return math.sqrt(ms_error(X, Y))\n",
    "\n",
    "\n",
    "distance = rms_error([1,0,5], [3,10,5])\n",
    "print(\"Root of Mean Error Distance between (1,0,5) and (3,10,5) is\", distance)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PLURALITY LEARNER CLASSIFIER\n",
    "\n",
    "### Overview\n",
    "\n",
    "The Plurality Learner is a simple algorithm, used mainly as a baseline comparison for other algorithms. It finds the most popular class in the dataset and classifies any subsequent item to that class. Essentially, it classifies every new item to the same class. For that reason, it is not used very often, instead opting for more complicated algorithms when we want accurate classification.\n",
    "\n",
    "![pL plot](images/pluralityLearner_plot.png)\n",
    "\n",
    "Let's see how the classifier works with the plot above. There are three classes named **Class A** (orange-colored dots) and **Class B** (blue-colored dots) and **Class C** (green-colored dots). Every point in this plot has two **features** (i.e. X<sub>1</sub>, X<sub>2</sub>). Now, let's say we have a new point, a red star and we want to know which class this red star belongs to. Solving this problem by predicting the class of this new red star is our current classification problem.\n",
    "\n",
    "The Plurality Learner will find the class most represented in the plot. ***Class A*** has four items, ***Class B*** has three and ***Class C*** has seven. The most popular class is ***Class C***. Therefore, the item will get classified in ***Class C***, despite the fact that it is closer to the other two classes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "Below follows the implementation of the PluralityLearner algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(PluralityLearner)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It takes as input a dataset and returns a function. We can later call this function with the item we want to classify as the argument and it returns the class it should be classified in.\n",
    "\n",
    "The function first finds the most popular class in the dataset and then each time we call its \"predict\" function, it returns it. Note that the input (\"example\") does not matter. The function always returns the same class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example\n",
    "\n",
    "For this example, we will not use the Iris dataset, since each class is represented the same. This will throw an error. Instead we will use the zoo dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mammal\n"
     ]
    }
   ],
   "source": [
    "zoo = DataSet(name=\"zoo\")\n",
    "\n",
    "pL = PluralityLearner(zoo)\n",
    "print(pL([1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 4, 1, 0, 1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output for the above code is \"mammal\", since that is the most popular and common class in the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## K-NEAREST NEIGHBOURS CLASSIFIER\n",
    "\n",
    "### Overview\n",
    "The k-Nearest Neighbors algorithm is a non-parametric method used for classification and regression. We are going to use this to classify Iris flowers. More about kNN on [Scholarpedia](http://www.scholarpedia.org/article/K-nearest_neighbor).\n",
    "\n",
    "![kNN plot](images/knn_plot.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see how kNN works with a simple plot shown in the above picture.\n",
    "\n",
    "We have co-ordinates (we call them **features** in Machine Learning) of this red star and we need to predict its class using the kNN algorithm. In this algorithm, the value of **k** is arbitrary. **k** is one of the **hyper parameters** for kNN algorithm. We choose this number based on our dataset and choosing a particular number is known as **hyper parameter tuning/optimising**. We learn more about this in coming topics.\n",
    "\n",
    "Let's put **k = 3**. It means you need to find 3-Nearest Neighbors of this red star and classify this new point into the majority class. Observe that smaller circle which contains three points other than **test point** (red star). As there are two violet points, which form the majority, we predict the class of red star as **violet- Class B**.\n",
    "\n",
    "Similarly if we put **k = 5**, you can observe that there are three yellow points, which form the majority. So, we classify our test point as **yellow- Class A**.\n",
    "\n",
    "In practical tasks, we iterate through a bunch of values for k (like [1, 3, 5, 10, 20, 50, 100]), see how it performs and select the best one. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "Below follows the implementation of the kNN algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(NearestNeighborLearner)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It takes as input a dataset and k (default value is 1) and it returns a function, which we can later use to classify a new item.\n",
    "\n",
    "To accomplish that, the function uses a heap-queue, where the items of the dataset are sorted according to their distance from *example* (the item to classify). We then take the k smallest elements from the heap-queue and we find the majority class. We classify the item to this class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example\n",
    "\n",
    "We measured a new flower with the following values: 5.1, 3.0, 1.1, 0.1. We want to classify that item/flower in a class. To do that, we write the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa\n"
     ]
    }
   ],
   "source": [
    "iris = DataSet(name=\"iris\")\n",
    "\n",
    "kNN = NearestNeighborLearner(iris,k=3)\n",
    "print(kNN([5.1,3.0,1.1,0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of the above code is \"setosa\", which means the flower with the above measurements is of the \"setosa\" species."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DECISION TREE LEARNER\n",
    "\n",
    "### Overview\n",
    "\n",
    "#### Decision Trees\n",
    "A decision tree is a flowchart that uses a tree of decisions and their possible consequences for classification. At each non-leaf node of the tree an attribute of the input is tested, based on which corresponding branch leading to a child-node is selected. At the leaf node the input is classified based on the class label of this leaf node. The paths from root to leaves represent classification rules based on which leaf nodes are assigned class labels.\n",
    "![perceptron](images/decisiontree_fruit.jpg)\n",
    "#### Decision Tree Learning\n",
    "Decision tree learning is the construction of a decision tree from class-labeled training data. The data is expected to be a tuple in which each record of the tuple is an attribute used for classification. The decision tree is built top-down, by choosing a variable at each step that best splits the set of items. There are different metrics for measuring the \"best split\". These generally measure the homogeneity of the target variable within the subsets.\n",
    "\n",
    "#### Gini Impurity\n",
    "Gini impurity of a set is the probability of a randomly chosen element to be incorrectly labeled if it was randomly labeled according to the distribution of labels in the set.\n",
    "\n",
    "$$I_G(p) = \\sum{p_i(1 - p_i)} = 1 - \\sum{p_i^2}$$\n",
    "\n",
    "We select a split which minimizes the Gini impurity in child nodes.\n",
    "\n",
    "#### Information Gain\n",
    "Information gain is based on the concept of entropy from information theory. Entropy is defined as:\n",
    "\n",
    "$$H(p) = -\\sum{p_i \\log_2{p_i}}$$\n",
    "\n",
    "Information Gain is difference between entropy of the parent and weighted sum of entropy of children. The feature used for splitting is the one which provides the most information gain.\n",
    "\n",
    "#### Pseudocode\n",
    "\n",
    "You can view the pseudocode by running the cell below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pseudocode(\"Decision Tree Learning\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "The nodes of the tree constructed by our learning algorithm are stored using either `DecisionFork` or `DecisionLeaf` based on whether they are a parent node or a leaf node respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "psource(DecisionFork)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`DecisionFork` holds the attribute, which is tested at that node, and a dict of branches. The branches store the child nodes, one for each of the attribute's values. Calling an object of this class as a function with input tuple as an argument returns the next node in the classification path based on the result of the attribute test."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(DecisionLeaf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The leaf node stores the class label in `result`. All input tuples' classification paths end on a `DecisionLeaf` whose `result` attribute decide their class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(DecisionTreeLearner)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The implementation of `DecisionTreeLearner` provided in [learning.py](https://github.com/aimacode/aima-python/blob/master/learning.py) uses information gain as the metric for selecting which attribute to test for splitting. The function builds the tree top-down in a recursive manner. Based on the input it makes one of the four choices:\n",
    "<ol>\n",
    "<li>If the input at the current step has no training data we return the mode of classes of input data received in the parent step (previous level of recursion).</li>\n",
    "<li>If all values in training data belong to the same class it returns a `DecisionLeaf` whose class label is the class which all the data belongs to.</li>\n",
    "<li>If the data has no attributes that can be tested we return the class with highest plurality value in the training data.</li>\n",
    "<li>We choose the attribute which gives the highest amount of entropy gain and return a `DecisionFork` which splits based on this attribute. Each branch recursively calls `decision_tree_learning` to construct the sub-tree.</li>\n",
    "</ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example\n",
    "\n",
    "We will now use the Decision Tree Learner to classify a sample with values: 5.1, 3.0, 1.1, 0.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa\n"
     ]
    }
   ],
   "source": [
    "iris = DataSet(name=\"iris\")\n",
    "\n",
    "DTL = DecisionTreeLearner(iris)\n",
    "print(DTL([5.1, 3.0, 1.1, 0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the Decision Tree learner classifies the sample as \"setosa\" as seen in the previous section."
   ]
  },
  {
   "attachments": {
    "0_tG-IWcxL1jg7RkT0.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RANDOM FOREST LEARNER\n",
    "\n",
    "### Overview\n",
    "\n",
    "![random_forest.png](images/random_forest.png)   \n",
    "Image via [src](https://cdn-images-1.medium.com/max/800/0*tG-IWcxL1jg7RkT0.png)\n",
    "\n",
    "#### Random Forest\n",
    "\n",
    "As the name of the algorithm and image above suggest, this algorithm creates the forest with a number of trees. The more number of trees makes the forest robust. In the same way in random forest algorithm, the higher the number of trees in the forest, the higher is the accuray result. The main difference between Random Forest and Decision trees is that, finding the root node and splitting the feature nodes will be random.  \n",
    "\n",
    "Let's see how Rnadom Forest Algorithm work :   \n",
    "Random Forest Algorithm works in two steps, first is the creation of random forest and then the prediction. Let's first see the creation :  \n",
    "\n",
    "The first step in creation is to randomly select 'm' features out of total 'n' features. From these 'm' features calculate the node d using the best split point and then split the node into further nodes using best split. Repeat these steps until 'i' number of nodes are reached. Repeat the entire whole process to build the forest.  \n",
    "\n",
    "Now, let's see how the prediction works\n",
    "Take the test features and predict the outcome for each randomly created decision tree. Calculate the votes for each prediction and the prediction which gets the highest votes would be the final prediction.\n",
    "\n",
    "\n",
    "### Implementation\n",
    "\n",
    "Below mentioned is the implementation of Random Forest Algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "psource(RandomForest)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This algorithm creates an ensemble of decision trees using bagging and feature bagging. It takes 'm' examples randomly from the total number of examples and then perform feature bagging with probability p to retain an attribute. All the predictors are predicted from the DecisionTreeLearner and then a final prediction is made.\n",
    "\n",
    "\n",
    "### Example\n",
    "\n",
    "We will now use the Random Forest to classify a sample with values: 5.1, 3.0, 1.1, 0.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['versicolor', 'setosa', 'setosa', 'setosa', 'setosa']\n",
      "setosa\n"
     ]
    }
   ],
   "source": [
    "iris = DataSet(name=\"iris\")\n",
    "\n",
    "DTL = RandomForest(iris)\n",
    "print(DTL([5.1, 3.0, 1.1, 0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the Random Forest classifies the sample as \"setosa\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NAIVE BAYES LEARNER\n",
    "\n",
    "### Overview\n",
    "\n",
    "#### Theory of Probabilities\n",
    "\n",
    "The Naive Bayes algorithm is a probabilistic classifier, making use of [Bayes' Theorem](https://en.wikipedia.org/wiki/Bayes%27_theorem). The theorem states that the conditional probability of **A** given **B** equals the conditional probability of **B** given **A** multiplied by the probability of **A**, divided by the probability of **B**.\n",
    "\n",
    "$$P(A|B) = \\dfrac{P(B|A)*P(A)}{P(B)}$$\n",
    "\n",
    "From the theory of Probabilities we have the Multiplication Rule, if the events *X* are independent the following is true:\n",
    "\n",
    "$$P(X_{1} \\cap X_{2} \\cap ... \\cap X_{n}) = P(X_{1})*P(X_{2})*...*P(X_{n})$$\n",
    "\n",
    "For conditional probabilities this becomes:\n",
    "\n",
    "$$P(X_{1}, X_{2}, ..., X_{n}|Y) = P(X_{1}|Y)*P(X_{2}|Y)*...*P(X_{n}|Y)$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classifying an Item\n",
    "\n",
    "How can we use the above to classify an item though?\n",
    "\n",
    "We have a dataset with a set of classes (**C**) and we want to classify an item with a set of features (**F**). Essentially what we want to do is predict the class of an item given the features.\n",
    "\n",
    "For a specific class, **Class**, we will find the conditional probability given the item features:\n",
    "\n",
    "$$P(Class|F) = \\dfrac{P(F|Class)*P(Class)}{P(F)}$$\n",
    "\n",
    "We will do this for every class and we will pick the maximum. This will be the class the item is classified in.\n",
    "\n",
    "The features though are a vector with many elements. We need to break the probabilities up using the multiplication rule. Thus the above equation becomes:\n",
    "\n",
    "$$P(Class|F) = \\dfrac{P(Class)*P(F_{1}|Class)*P(F_{2}|Class)*...*P(F_{n}|Class)}{P(F_{1})*P(F_{2})*...*P(F_{n})}$$\n",
    "\n",
    "The calculation of the conditional probability then depends on the calculation of the following:\n",
    "\n",
    "*a)* The probability of **Class** in the dataset.\n",
    "\n",
    "*b)* The conditional probability of each feature occurring in an item classified in **Class**.\n",
    "\n",
    "*c)* The probabilities of each individual feature.\n",
    "\n",
    "For *a)*, we will count how many times **Class** occurs in the dataset (aka how many items are classified in a particular class).\n",
    "\n",
    "For *b)*, if the feature values are discrete ('Blue', '3', 'Tall', etc.), we will count how many times a feature value occurs in items of each class. If the feature values are not discrete, we will go a different route. We will use a distribution function to calculate the probability of values for a given class and feature. If we know the distribution function of the dataset, then great, we will use it to compute the probabilities. If we don't know the function, we can assume the dataset follows the normal (Gaussian) distribution without much loss of accuracy. In fact, it can be proven that any distribution tends to the Gaussian the larger the population gets (see [Central Limit Theorem](https://en.wikipedia.org/wiki/Central_limit_theorem)).\n",
    "\n",
    "*NOTE:* If the values are continuous but use the discrete approach, there might be issues if we are not lucky. For one, if we have two values, '5.0 and 5.1', with the discrete approach they will be two completely different values, despite being so close. Second, if we are trying to classify an item with a feature value of '5.15', if the value does not appear for the feature, its probability will be 0. This might lead to misclassification. Generally, the continuous approach is more accurate and more useful, despite the overhead of calculating the distribution function.\n",
    "\n",
    "The last one, *c)*, is tricky. If feature values are discrete, we can count how many times they occur in the dataset. But what if the feature values are continuous? Imagine a dataset with a height feature. Is it worth it to count how many times each value occurs? Most of the time it is not, since there can be miscellaneous differences in the values (for example, 1.7 meters and 1.700001 meters are practically equal, but they count as different values).\n",
    "\n",
    "So as we cannot calculate the feature value probabilities, what are we going to do?\n",
    "\n",
    "Let's take a step back and rethink exactly what we are doing. We are essentially comparing conditional probabilities of all the classes. For two classes, **A** and **B**, we want to know which one is greater:\n",
    "\n",
    "$$\\dfrac{P(F|A)*P(A)}{P(F)} vs. \\dfrac{P(F|B)*P(B)}{P(F)}$$\n",
    "\n",
    "Wait, **P(F)** is the same for both the classes! In fact, it is the same for every combination of classes. That is because **P(F)** does not depend on a class, thus being independent of the classes.\n",
    "\n",
    "So, for *c)*, we actually don't need to calculate it at all."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Wrapping It Up\n",
    "\n",
    "Classifying an item to a class then becomes a matter of calculating the conditional probabilities of feature values and the probabilities of classes. This is something very desirable and computationally delicious.\n",
    "\n",
    "Remember though that all the above are true because we made the assumption that the features are independent. In most real-world cases that is not true though. Is that an issue here? Fret not, for the the algorithm is very efficient even with that assumption. That is why the algorithm is called **Naive** Bayes Classifier. We (naively) assume that the features are independent to make computations easier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "The implementation of the Naive Bayes Classifier is split in two; *Learning* and *Simple*. The *learning* classifier takes as input a dataset and learns the needed distributions from that. It is itself split into two, for discrete and continuous features. The *simple* classifier takes as input not a dataset, but already calculated distributions (a dictionary of `CountingProbDist` objects)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discrete\n",
    "\n",
    "The implementation for discrete values counts how many times each feature value occurs for each class, and how many times each class occurs. The results are stored in a `CountinProbDist` object."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the below code you can see the probabilities of the class \"Setosa\" appearing in the dataset and the probability of the first feature (at index 0) of the same class having a value of 5. Notice that the second probability is relatively small, even though if we observe the dataset we will find that a lot of values are around 5. The issue arises because the features in the Iris dataset are continuous, and we are assuming they are discrete. If the features were discrete (for example, \"Tall\", \"3\", etc.) this probably wouldn't have been the case and we would see a much nicer probability distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.3333333333333333\n",
      "0.10588235294117647\n"
     ]
    }
   ],
   "source": [
    "dataset = iris\n",
    "\n",
    "target_vals = dataset.values[dataset.target]\n",
    "target_dist = CountingProbDist(target_vals)\n",
    "attr_dists = {(gv, attr): CountingProbDist(dataset.values[attr])\n",
    "              for gv in target_vals\n",
    "              for attr in dataset.inputs}\n",
    "for example in dataset.examples:\n",
    "        targetval = example[dataset.target]\n",
    "        target_dist.add(targetval)\n",
    "        for attr in dataset.inputs:\n",
    "            attr_dists[targetval, attr].add(example[attr])\n",
    "\n",
    "\n",
    "print(target_dist['setosa'])\n",
    "print(attr_dists['setosa', 0][5.0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we found the different values for the classes (called targets here) and calculated their distribution. Next we initialized a dictionary of `CountingProbDist` objects, one for each class and feature. Finally, we iterated through the examples in the dataset and calculated the needed probabilites.\n",
    "\n",
    "Having calculated the different probabilities, we will move on to the predicting function. It will receive as input an item and output the most likely class. Using the above formula, it will multiply the probability of the class appearing, with the probability of each feature value appearing in the class. It will return the max result."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa\n"
     ]
    }
   ],
   "source": [
    "def predict(example):\n",
    "    def class_probability(targetval):\n",
    "        return (target_dist[targetval] *\n",
    "                product(attr_dists[targetval, attr][example[attr]]\n",
    "                        for attr in dataset.inputs))\n",
    "    return argmax(target_vals, key=class_probability)\n",
    "\n",
    "\n",
    "print(predict([5, 3, 1, 0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can view the complete code by executing the next line:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(NaiveBayesDiscrete)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Continuous\n",
    "\n",
    "In the implementation we use the Gaussian/Normal distribution function. To make it work, we need to find the means and standard deviations of features for each class. We make use of the `find_means_and_deviations` Dataset function. On top of that, we will also calculate the class probabilities as we did with the Discrete approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.006, 3.418, 1.464, 0.244]\n",
      "[0.5161711470638634, 0.3137983233784114, 0.46991097723995795, 0.19775268000454405]\n"
     ]
    }
   ],
   "source": [
    "means, deviations = dataset.find_means_and_deviations()\n",
    "\n",
    "target_vals = dataset.values[dataset.target]\n",
    "target_dist = CountingProbDist(target_vals)\n",
    "\n",
    "\n",
    "print(means[\"setosa\"])\n",
    "print(deviations[\"versicolor\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see the means of the features for the \"Setosa\" class and the deviations for \"Versicolor\".\n",
    "\n",
    "The prediction function will work similarly to the Discrete algorithm. It will multiply the probability of the class occurring with the conditional probabilities of the feature values for the class.\n",
    "\n",
    "Since we are using the Gaussian distribution, we will input the value for each feature into the Gaussian function, together with the mean and deviation of the feature. This will return the probability of the particular feature value for the given class. We will repeat for each class and pick the max value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa\n"
     ]
    }
   ],
   "source": [
    "def predict(example):\n",
    "    def class_probability(targetval):\n",
    "        prob = target_dist[targetval]\n",
    "        for attr in dataset.inputs:\n",
    "            prob *= gaussian(means[targetval][attr], deviations[targetval][attr], example[attr])\n",
    "        return prob\n",
    "\n",
    "    return argmax(target_vals, key=class_probability)\n",
    "\n",
    "\n",
    "print(predict([5, 3, 1, 0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The complete code of the continuous algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(NaiveBayesContinuous)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simple\n",
    "\n",
    "The simple classifier (chosen with the argument `simple`) does not learn from a dataset, instead it takes as input a dictionary of already calculated `CountingProbDist` objects and returns a predictor function. The dictionary is in the following form: `(Class Name, Class Probability): CountingProbDist Object`.\n",
    "\n",
    "Each class has its own probability distribution. The classifier given a list of features calculates the probability of the input for each class and returns the max. The only pre-processing work is to create dictionaries for the distribution of classes (named `targets`) and attributes/features.\n",
    "\n",
    "The complete code for the simple classifier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(NaiveBayesSimple)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This classifier is useful when you already have calculated the distributions and you need to predict future items."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examples\n",
    "\n",
    "We will now use the Naive Bayes Classifier (Discrete and Continuous) to classify items:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Discrete Classifier\n",
      "setosa\n",
      "setosa\n",
      "setosa\n",
      "\n",
      "Continuous Classifier\n",
      "setosa\n",
      "versicolor\n",
      "virginica\n"
     ]
    }
   ],
   "source": [
    "nBD = NaiveBayesLearner(iris, continuous=False)\n",
    "print(\"Discrete Classifier\")\n",
    "print(nBD([5, 3, 1, 0.1]))\n",
    "print(nBD([6, 5, 3, 1.5]))\n",
    "print(nBD([7, 3, 6.5, 2]))\n",
    "\n",
    "\n",
    "nBC = NaiveBayesLearner(iris, continuous=True)\n",
    "print(\"\\nContinuous Classifier\")\n",
    "print(nBC([5, 3, 1, 0.1]))\n",
    "print(nBC([6, 5, 3, 1.5]))\n",
    "print(nBC([7, 3, 6.5, 2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the Discrete Classifier misclassified the second item, while the Continuous one had no problem.\n",
    "\n",
    "Let's now take a look at the simple classifier. First we will come up with a sample problem to solve. Say we are given three bags. Each bag contains three letters ('a', 'b' and 'c') of different quantities. We are given a string of letters and we are tasked with finding from which bag the string of letters came.\n",
    "\n",
    "Since we know the probability distribution of the letters for each bag, we can use the naive bayes classifier to make our prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bag1 = 'a'*50 + 'b'*30 + 'c'*15\n",
    "dist1 = CountingProbDist(bag1)\n",
    "bag2 = 'a'*30 + 'b'*45 + 'c'*20\n",
    "dist2 = CountingProbDist(bag2)\n",
    "bag3 = 'a'*20 + 'b'*20 + 'c'*35\n",
    "dist3 = CountingProbDist(bag3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the `CountingProbDist` objects for each bag/class, we will create the dictionary. We assume that it is equally probable that we will pick from any bag."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dist = {('First', 0.5): dist1, ('Second', 0.3): dist2, ('Third', 0.2): dist3}\n",
    "nBS = NaiveBayesLearner(dist, simple=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can start making predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First\n",
      "Second\n",
      "Third\n"
     ]
    }
   ],
   "source": [
    "print(nBS('aab'))        # We can handle strings\n",
    "print(nBS(['b', 'b']))   # And lists!\n",
    "print(nBS('ccbcc'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The results make intuitive sence. The first bag has a high amount of 'a's, the second has a high amount of 'b's and the third has a high amount of 'c's. The classifier seems to confirm this intuition.\n",
    "\n",
    "Note that the simple classifier doesn't distinguish between discrete and continuous values. It just takes whatever it is given. Also, the `simple` option on the `NaiveBayesLearner` overrides the `continuous` argument. `NaiveBayesLearner(d, simple=True, continuous=False)` just creates a simple classifier."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PERCEPTRON CLASSIFIER\n",
    "\n",
    "### Overview\n",
    "\n",
    "The Perceptron is a linear classifier. It works the same way as a neural network with no hidden layers (just input and output). First it trains its weights given a dataset and then it can classify a new item by running it through the network.\n",
    "\n",
    "Its input layer consists of the the item features, while the output layer consists of nodes (also called neurons). Each node in the output layer has *n* synapses (for every item feature), each with its own weight. Then, the nodes find the dot product of the item features and the synapse weights. These values then pass through an activation function (usually a sigmoid). Finally, we pick the largest of the values and we return its index.\n",
    "\n",
    "Note that in classification problems each node represents a class. The final classification is the class/node with the max output value.\n",
    "\n",
    "Below you can see a single node/neuron in the outer layer. With *f* we denote the item features, with *w* the synapse weights, then inside the node we have the dot product and the activation function, *g*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![perceptron](images/perceptron.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "First, we train (calculate) the weights given a dataset, using the `BackPropagationLearner` function of `learning.py`. We then return a function, `predict`, which we will use in the future to classify a new item. The function computes the (algebraic) dot product of the item with the calculated weights for each node in the outer layer. Then it picks the greatest value and classifies the item in the corresponding class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "psource(PerceptronLearner)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the Perceptron is a one-layer neural network, without any hidden layers. So, in `BackPropagationLearner`, we will pass no hidden layers. From that function we get our network, which is just one layer, with the weights calculated.\n",
    "\n",
    "That function `predict` passes the input/example through the network, calculating the dot product of the input and the weights for each node and returns the class with the max dot product."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example\n",
    "\n",
    "We will train the Perceptron on the iris dataset. Because though the `BackPropagationLearner` works with integer indexes and not strings, we need to convert class names to integers. Then, we will try and classify the item/flower with measurements of 5, 3, 1, 0.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "iris = DataSet(name=\"iris\")\n",
    "iris.classes_to_numbers()\n",
    "\n",
    "perceptron = PerceptronLearner(iris)\n",
    "print(perceptron([5, 3, 1, 0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The correct output is 0, which means the item belongs in the first class, \"setosa\". Note that the Perceptron algorithm is not perfect and may produce false classifications."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LINEAR LEARNER\n",
    "\n",
    "### Overview\n",
    "\n",
    "Linear Learner is a model that assumes a linear relationship between the input variables x and the single output variable y. More specifically, that y can be calculated from a linear combination of the input variables x. Linear learner is a quite simple model as the representation of this model is a linear equation.  \n",
    "\n",
    "The linear equation assigns one scaler factor to each input value or column, called a coefficients or weights. One additional coefficient is also added, giving additional degree of freedom and is often called the intercept or the bias coefficient.   \n",
    "For example :  y = ax1 + bx2 + c .  \n",
    "\n",
    "### Implementation\n",
    "\n",
    "Below mentioned is the implementation of Linear Learner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "psource(LinearLearner)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This algorithm first assigns some random weights to the input variables and then based on the error calculated updates the weight for each variable. Finally the prediction is made with the updated weights.  \n",
    "\n",
    "### Implementation\n",
    "\n",
    "We will now use the Linear Learner to classify a sample with values: 5.1, 3.0, 1.1, 0.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2404650656510341\n"
     ]
    }
   ],
   "source": [
    "iris = DataSet(name=\"iris\")\n",
    "iris.classes_to_numbers()\n",
    "\n",
    "linear_learner = LinearLearner(iris)\n",
    "print(linear_learner([5, 3, 1, 0.1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ENSEMBLE LEARNER\n",
    "\n",
    "### Overview\n",
    "\n",
    "Ensemble Learning improves the performance of our model by combining several learners. It improvise the stability and predictive power of the model. Ensemble methods are meta-algorithms that combine several machine learning techniques into one predictive model in order to decrease variance, bias, or improve predictions.  \n",
    "\n",
    "\n",
    "\n",
    "![ensemble_learner.jpg](images/ensemble_learner.jpg)\n",
    "\n",
    "\n",
    "Some commonly used Ensemble Learning techniques are : \n",
    "\n",
    "1. Bagging : Bagging tries to implement similar learners on small sample populations and then takes a mean of all the predictions. It helps us to reduce variance error.\n",
    "\n",
    "2. Boosting : Boosting is an iterative technique which adjust the weight of an observation based on the last classification. If an observation was classified incorrectly, it tries to increase the weight of this observation and vice versa. It helps us to reduce bias error.\n",
    "\n",
    "3.  Stacking : This is a very interesting way of combining models. Here we use a learner to combine output from different learners. It can either decrease bias or variance error depending on the learners we use.\n",
    "\n",
    "### Implementation\n",
    "\n",
    "Below mentioned is the implementation of Ensemble Learner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "psource(EnsembleLearner)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This algorithm takes input as a list of learning algorithms, have them vote and then finally returns the predicted result."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LEARNER EVALUATION\n",
    "\n",
    "In this section we will evaluate and compare algorithm performance. The dataset we will use will again be the iris one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "iris = DataSet(name=\"iris\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Naive Bayes\n",
    "\n",
    "First up we have the Naive Bayes algorithm. First we will test how well the Discrete Naive Bayes works, and then how the Continuous fares."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error ratio for Discrete: 0.040000000000000036\n",
      "Error ratio for Continuous: 0.040000000000000036\n"
     ]
    }
   ],
   "source": [
    "nBD = NaiveBayesLearner(iris, continuous=False)\n",
    "print(\"Error ratio for Discrete:\", err_ratio(nBD, iris))\n",
    "\n",
    "nBC = NaiveBayesLearner(iris, continuous=True)\n",
    "print(\"Error ratio for Continuous:\", err_ratio(nBC, iris))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The error for the Naive Bayes algorithm is very, very low; close to 0. There is also very little difference between the discrete and continuous version of the algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## k-Nearest Neighbors\n",
    "\n",
    "Now we will take a look at kNN, for different values of *k*. Note that *k* should have odd values, to break any ties between two classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error ratio for k=1: 0.0\n",
      "Error ratio for k=3: 0.06000000000000005\n",
      "Error ratio for k=5: 0.1266666666666667\n",
      "Error ratio for k=7: 0.19999999999999996\n"
     ]
    }
   ],
   "source": [
    "kNN_1 = NearestNeighborLearner(iris, k=1)\n",
    "kNN_3 = NearestNeighborLearner(iris, k=3)\n",
    "kNN_5 = NearestNeighborLearner(iris, k=5)\n",
    "kNN_7 = NearestNeighborLearner(iris, k=7)\n",
    "\n",
    "print(\"Error ratio for k=1:\", err_ratio(kNN_1, iris))\n",
    "print(\"Error ratio for k=3:\", err_ratio(kNN_3, iris))\n",
    "print(\"Error ratio for k=5:\", err_ratio(kNN_5, iris))\n",
    "print(\"Error ratio for k=7:\", err_ratio(kNN_7, iris))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice how the error became larger and larger as *k* increased. This is generally the case with datasets where classes are spaced out, as is the case with the iris dataset. If items from different classes were closer together, classification would be more difficult. Usually a value of 1, 3 or 5 for *k* suffices.\n",
    "\n",
    "Also note that since the training set is also the testing set, for *k* equal to 1 we get a perfect score, since the item we want to classify each time is already in the dataset and its closest neighbor is itself."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perceptron\n",
    "\n",
    "For the Perceptron, we first need to convert class names to integers. Let's see how it performs in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error ratio for Perceptron: 0.31333333333333335\n"
     ]
    }
   ],
   "source": [
    "iris2 = DataSet(name=\"iris\")\n",
    "iris2.classes_to_numbers()\n",
    "\n",
    "perceptron = PerceptronLearner(iris2)\n",
    "print(\"Error ratio for Perceptron:\", err_ratio(perceptron, iris2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Perceptron didn't fare very well mainly because the dataset is not linearly separated. On simpler datasets the algorithm performs much better, but unfortunately such datasets are rare in real life scenarios."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AdaBoost\n",
    "\n",
    "### Overview\n",
    "\n",
    "**AdaBoost** is an algorithm which uses **ensemble learning**. In ensemble learning the hypotheses in the collection, or ensemble, vote for what the output should be and the output with the majority votes is selected as the final answer.\n",
    "\n",
    "AdaBoost algorithm, as mentioned in the book, works with a **weighted training set** and **weak learners** (classifiers that have about 50%+epsilon accuracy i.e slightly better than random guessing). It manipulates the weights attached to the the examples that are showed to it. Importance is given to the examples with higher weights.\n",
    "\n",
    "All the examples start with equal weights and a hypothesis is generated using these examples. Examples which are incorrectly classified, their weights are increased so that they can be classified correctly by the next hypothesis. The examples that are correctly classified, their weights are reduced. This process is repeated *K* times (here *K* is an input to the algorithm) and hence, *K* hypotheses are generated.\n",
    "\n",
    "These *K* hypotheses are also assigned weights according to their performance on the weighted training set. The final ensemble hypothesis is the weighted-majority combination of these *K* hypotheses.\n",
    "\n",
    "The speciality of AdaBoost is that by using weak learners and a sufficiently large *K*, a highly accurate classifier can be learned irrespective of the complexity of the function being learned or the dullness of the hypothesis space."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementation\n",
    "\n",
    "As seen in the previous section, the `PerceptronLearner` does not perform that well on the iris dataset. We'll use perceptron as the learner for the AdaBoost algorithm and try to increase the accuracy. \n",
    "\n",
    "Let's first see what AdaBoost is exactly:"
   ]
  },
  {
   "cell_type": "code",
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       "\n",
       "<div class=\"highlight\"><pre><span></span><span class=\"k\">def</span> <span class=\"nf\">AdaBoost</span><span class=\"p\">(</span><span class=\"n\">L</span><span class=\"p\">,</span> <span class=\"n\">K</span><span class=\"p\">):</span>\n",
       "    <span class=\"sd\">&quot;&quot;&quot;[Figure 18.34]&quot;&quot;&quot;</span>\n",
       "    <span class=\"k\">def</span> <span class=\"nf\">train</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">):</span>\n",
       "        <span class=\"n\">examples</span><span class=\"p\">,</span> <span class=\"n\">target</span> <span class=\"o\">=</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">examples</span><span class=\"p\">,</span> <span class=\"n\">dataset</span><span class=\"o\">.</span><span class=\"n\">target</span>\n",
       "        <span class=\"n\">N</span> <span class=\"o\">=</span> <span class=\"nb\">len</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">)</span>\n",
       "        <span class=\"n\">epsilon</span> <span class=\"o\">=</span> <span class=\"mf\">1.</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"mi\">2</span> <span class=\"o\">*</span> <span class=\"n\">N</span><span class=\"p\">)</span>\n",
       "        <span class=\"n\">w</span> <span class=\"o\">=</span> <span class=\"p\">[</span><span class=\"mf\">1.</span> <span class=\"o\">/</span> <span class=\"n\">N</span><span class=\"p\">]</span> <span class=\"o\">*</span> <span class=\"n\">N</span>\n",
       "        <span class=\"n\">h</span><span class=\"p\">,</span> <span class=\"n\">z</span> <span class=\"o\">=</span> <span class=\"p\">[],</span> <span class=\"p\">[]</span>\n",
       "        <span class=\"k\">for</span> <span class=\"n\">k</span> <span class=\"ow\">in</span> <span class=\"nb\">range</span><span class=\"p\">(</span><span class=\"n\">K</span><span class=\"p\">):</span>\n",
       "            <span class=\"n\">h_k</span> <span class=\"o\">=</span> <span class=\"n\">L</span><span class=\"p\">(</span><span class=\"n\">dataset</span><span class=\"p\">,</span> <span class=\"n\">w</span><span class=\"p\">)</span>\n",
       "            <span class=\"n\">h</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">h_k</span><span class=\"p\">)</span>\n",
       "            <span class=\"n\">error</span> <span class=\"o\">=</span> <span class=\"nb\">sum</span><span class=\"p\">(</span><span class=\"n\">weight</span> <span class=\"k\">for</span> <span class=\"n\">example</span><span class=\"p\">,</span> <span class=\"n\">weight</span> <span class=\"ow\">in</span> <span class=\"nb\">zip</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">,</span> <span class=\"n\">w</span><span class=\"p\">)</span>\n",
       "                        <span class=\"k\">if</span> <span class=\"n\">example</span><span class=\"p\">[</span><span class=\"n\">target</span><span class=\"p\">]</span> <span class=\"o\">!=</span> <span class=\"n\">h_k</span><span class=\"p\">(</span><span class=\"n\">example</span><span class=\"p\">))</span>\n",
       "            <span class=\"c1\"># Avoid divide-by-0 from either 0% or 100% error rates:</span>\n",
       "            <span class=\"n\">error</span> <span class=\"o\">=</span> <span class=\"n\">clip</span><span class=\"p\">(</span><span class=\"n\">error</span><span class=\"p\">,</span> <span class=\"n\">epsilon</span><span class=\"p\">,</span> <span class=\"mi\">1</span> <span class=\"o\">-</span> <span class=\"n\">epsilon</span><span class=\"p\">)</span>\n",
       "            <span class=\"k\">for</span> <span class=\"n\">j</span><span class=\"p\">,</span> <span class=\"n\">example</span> <span class=\"ow\">in</span> <span class=\"nb\">enumerate</span><span class=\"p\">(</span><span class=\"n\">examples</span><span class=\"p\">):</span>\n",
       "                <span class=\"k\">if</span> <span class=\"n\">example</span><span class=\"p\">[</span><span class=\"n\">target</span><span class=\"p\">]</span> <span class=\"o\">==</span> <span class=\"n\">h_k</span><span class=\"p\">(</span><span class=\"n\">example</span><span class=\"p\">):</span>\n",
       "                    <span class=\"n\">w</span><span class=\"p\">[</span><span class=\"n\">j</span><span class=\"p\">]</span> <span class=\"o\">*=</span> <span class=\"n\">error</span> <span class=\"o\">/</span> <span class=\"p\">(</span><span class=\"mf\">1.</span> <span class=\"o\">-</span> <span class=\"n\">error</span><span class=\"p\">)</span>\n",
       "            <span class=\"n\">w</span> <span class=\"o\">=</span> <span class=\"n\">normalize</span><span class=\"p\">(</span><span class=\"n\">w</span><span class=\"p\">)</span>\n",
       "            <span class=\"n\">z</span><span class=\"o\">.</span><span class=\"n\">append</span><span class=\"p\">(</span><span class=\"n\">math</span><span class=\"o\">.</span><span class=\"n\">log</span><span class=\"p\">((</span><span class=\"mf\">1.</span> <span class=\"o\">-</span> <span class=\"n\">error</span><span class=\"p\">)</span> <span class=\"o\">/</span> <span class=\"n\">error</span><span class=\"p\">))</span>\n",
       "        <span class=\"k\">return</span> <span class=\"n\">WeightedMajority</span><span class=\"p\">(</span><span class=\"n\">h</span><span class=\"p\">,</span> <span class=\"n\">z</span><span class=\"p\">)</span>\n",
       "    <span class=\"k\">return</span> <span class=\"n\">train</span>\n",
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    "psource(AdaBoost)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "AdaBoost takes as inputs: **L** and *K* where **L** is the learner and *K* is the number of hypotheses to be generated. The learner **L** takes in as inputs: a dataset and the weights associated with the examples in the dataset. But the `PerceptronLearner` doesnot handle weights and only takes a dataset as its input.  \n",
    "To remedy that we will give as input to the PerceptronLearner a modified dataset in which the examples will be repeated according to the weights associated to them. Intuitively, what this will do is force the learner to repeatedly learn the same example again and again until it can classify it correctly.   \n",
    "\n",
    "To convert `PerceptronLearner` so that it can take weights as input too, we will have to pass it through the **`WeightedLearner`** function."
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "psource(WeightedLearner)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `WeightedLearner` function will then call the `PerceptronLearner`, during each iteration, with the modified dataset which contains the examples according to the weights associated with them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Example\n",
    "\n",
    "We will pass the `PerceptronLearner` through `WeightedLearner` function. Then we will create an `AdaboostLearner` classifier with number of hypotheses or *K* equal to 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
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   "source": [
    "WeightedPerceptron = WeightedLearner(PerceptronLearner)\n",
    "AdaboostLearner = AdaBoost(WeightedPerceptron, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
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   "source": [
    "iris2 = DataSet(name=\"iris\")\n",
    "iris2.classes_to_numbers()\n",
    "\n",
    "adaboost = AdaboostLearner(iris2)\n",
    "\n",
    "adaboost([5, 3, 1, 0.1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That is the correct answer. Let's check the error rate of adaboost with perceptron."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error ratio for adaboost:  0.046666666666666634\n"
     ]
    }
   ],
   "source": [
    "print(\"Error ratio for adaboost: \", err_ratio(adaboost, iris2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "It reduced the error rate considerably. Unlike the `PerceptronLearner`, `AdaBoost` was able to learn the complexity in the iris dataset."
   ]
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